The present invention is in the field of image descriptors and image processing. More specifically, the present invention relates to a technique for image descriptor adjustment when using steerable pyramids to extract image features for content-based search.
As image processing applications become more complex, an image search engine needs to be able to search and retrieve information about images effectively and efficiently. Images are often retrieved from a database by similarity of image features. Image processing allows for the comparison of a reference image against another image or multiple images in order to determine a xe2x80x9cmatchxe2x80x9d or correlation between the respective images. Accordingly, a variety of different image-matching techniques have been employed to determine such a match or correlation between images.
One such image matching technique is known as object classification. The object classification technique operates by segmenting the original image into a series of discrete objects. These discrete objects are then measured using a variety of shape measurement identifications, such as shape dimensions and statistics, to identify each discrete object. Accordingly, each of the discrete objects are then classified into different categories by comparing the shape measurement identifications associated with each of the discrete objects against known shape measurement identifications of known reference objects. As such, the shape measurement identifications associated with each of the discrete objects are compared against known shape measurement identifications of known reference objects in order to determine a correlation or match between the images.
Another image matching technique utilized in determining a match between images is a process known as match filtering. Match filtering utilizes a pixel-by-pixel or image mask comparison of an area of interest associated with the proffered image against a corresponding interest area contained in the reference image. Accordingly, provided the area of interest associated with the proffered image matches the corresponding interest area of the reference image, via comparison, an area or pixel match between the images is accomplished and the images are considered to match.
Yet another technique utilizes a series of textual descriptors which are associated with different reference images. The textual descriptors describe the image with textual descriptions, such as shape (e.g., round), color (e.g., green), and item (e.g., ball). Accordingly, when a proffered image is received for comparison, the textual descriptor of the proffered image is compared against the textual descriptors associated with the reference images. As such, the textual descriptor associated with the respective images under comparison are compared to each other in order to determine a best match between the textual descriptions associated with each image, and therefore a match between the respective images.
Each of the aforementioned image matching techniques uses different types of data or partial image data to describe the images. However, these techniques typically may not use the actual full image data associated with the each image. Accordingly, these techniques do not provide for an optimally accurate image comparison since they are limited to the usage of only a small or fractional portion of the full representative image data. Thus, when a search for similar images is conducted against a basis image, these techniques often result in the matching of very different images as having a correlation to one another. This partial-matching result is due in part by the limited amount or type of data used in the image comparison process.
A method of creating image descriptors by applying steerable filter to Laplacian images of a steerable pyramid is disclosed. The Laplacian images are generated by two corresponding Gaussian images in the steerable pyramid. If the Laplacian images possess negativity, they are adjusted accordingly to eliminate the negativity. Steerable filters are applied to the non-negative Laplacian images to generate orientation data and energy data. The adjustment made to the Laplacian images are correspondingly removed from the orientation data and the energy data.